$\Gamma$-VAE: Curvature regularized variational autoencoders for uncovering emergent low dimensional geometric structure in high dimensional data

arXiv:2403.01078v1 Announce Type: new
Abstract: Natural systems with emergent behaviors often organize along low-dimensional subsets of high-dimensional spaces. For example, despite the tens of thousands of genes in the human genome, the principled study of genomics is fruitful because biological processes rely on coordinated organization that results in lower dimensional phenotypes. To uncover this organization, many nonlinear dimensionality reduction techniques have successfully embedded high-dimensional data into low-dimensional spaces by preserving local similarities between data points. However, the nonlinearities in these …

abstract arxiv autoencoders cs.ai cs.lg data example genes genome genomics human low natural organize physics.bio-ph processes q-bio.gn spaces study subsets systems type vae variational autoencoders